Discrimination among similarly colored goose species in federal harvest surveys
Bibliographic record
Abstract
Abstract Each year in the United States, fall‐winter (sport) harvests of goose species are estimated from federal surveys coordinated by the United States Fish and Wildlife Service, including the Migratory Bird Harvest Survey to estimate total goose harvest and the Parts Collection Survey (PCS) to estimate the species and age composition. For the PCS, randomly selected hunters collect tail and wing feathers of each goose shot during the hunting season, and then biologists determine the age class and species of each sample at organized events (Wingbees) in each of the 4 flyways (Pacific, Central, Mississippi, and Atlantic). For similarly colored goose species, cackling (Branta hutchinsii) versus Canada (B. canadensis) geese (dark geese) and Ross's (Anser rossii) versus snow (A. caerulescens) geese (light geese), different protocols evolved among Wingbees to differentiate samples into groupings of management interest, leading to difficulties in estimating species‐level harvests among the 4 flyways or nationally. We conducted a study among the United States flyways during 2019–2022 to derive thresholds of central tail feather length to discriminate between dark geese and between light geese. We compared morphological‐ and genetic‐based approaches. There was support for 2 distinct mitochondrial DNA (mtDNA) clades in dark and light geese, but only dark goose clades corresponded with central tail feather lengths (morphological size and species identification). Derived thresholds for central tail feather lengths of dark geese in the 3 westernmost flyways using genetic‐based species' discrimination were 145 mm for adults and 134 mm for juveniles, approximately 13 mm and 9 mm less, respectively, than thresholds using morphological‐based species' discrimination. There was limited ability to discriminate light geese based on either mtDNA or central tail feather lengths. We suggest managers use our derived thresholds based on genetic‐based species' discrimination to classify dark goose PCS samples. More advanced genome analyses should be conducted before changing current Wingbee protocols for light geese. Lastly, we encourage more studies to incorporate genetic analyses to complement morphological discrimination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".